Perceived COVID‐19 health and job risks faced by digital platform drivers and measures in place to protect them: A qualitative study
Bibliographic record
Abstract
INTRODUCTION: As they deliver food, packages, and people across cities, digital platform drivers (gig workers) are in a key position to become infected with COVID-19 and transmit it to many others. The aim of this study is to identify perceived COVID-19 exposure and job risks faced by workers and document the measures in place to protect their health, and how workers responded to these measures. METHODS: In 2020-2021, in-depth interviews were conducted in Ontario, Canada, with 33 digital platform drivers and managers across nine platforms that delivered food, packages, or people. Interviews focused on perceived COVID-19 risks and mitigation strategies. Audio recordings were transcribed verbatim and uploaded to NVivo software for coding by varied dual pairs of researchers. A Stakeholder Advisory Committee played an instrumental role in the study. RESULTS: As self-employed workers were without the protection of employment and occupational health standards, platform workers absorbed most of the occupational risks related to COVID-19. Despite safety measures (e.g., contactless delivery) and financial support for COVID-19 illnesses introduced by platform companies, perceived COVID-19 risks remained high because of platform-related work pressures, including rating systems. We identify five key COVID-19 related risks faced by the digital platform drivers. CONCLUSION: We situate platform drivers within the broad context of precarious employment and recommend organizational- and government-level interventions to prevent digital platform worker COVID-19 risks and to assist workers ill with COVID-19. Measures to protect the health of platform workers would benefit public health aims by reducing transmission by drivers to families, customers, and consequently, the greater population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".